code: def test_with_cache(transform):
      if transform._cache_size == 0:
          transform = transform.with_cache(1)
      assert transform._cache_size == 1
      x = generate_data(transform).requires_grad_()
      try:
          y = transform(x)
      except NotImplementedError:
          pytest.skip("Not implemented.")
      y2 = transform(x)
      assert y2 is y
  
imports: import io
  from numbers import Number
  import pytest
  import torch
  from torch.autograd import grad
  from torch.autograd.functional import jacobian
  from torch.distributions import Dirichlet, Independent, Normal, TransformedDistribution, constraints
  from torch.distributions.transforms import AbsTransform, AffineTransform, ComposeTransform, CorrCholeskyTransform, CumulativeDistributionTransform, ExpTransform, IndependentTransform, LowerCholeskyTransform, PowerTransform, ReshapeTransform, SigmoidTransform, TanhTransform, SoftmaxTransform, SoftplusTransform, StickBreakingTransform, identity_transform, Transform, _InverseTransform, PositiveDefiniteTransform
  from torch.distributions.utils import tril_matrix_to_vec, vec_to_tril_matrix
  from torch.testing._internal.common_utils import run_tests
label_desc: Use image node_id 7 for calling a global function with example usage: test_with_cache(transform) without return types